With the rapid development of remote sensing technology, remote sensing data is constantly developing towards high time, high space and high spectral resolution. For the obtaining of traditional remote sensing data, it is difficult to obtain images with high time and high spectral resolution simultaneously due to the limitation of the remote sensing technology. Nowadays, it is possible to obtain the data with high time, high space and high spectral resolution simultaneously through data processing, so as to meet the urgent need for high time, high space, and high spectral resolution remote sensing data of the remote sensing applications. Research and analysis based on time dimension and spectral dimension have become the hot spot in remote sensing study.
In traditional remote sensing study, it is developed from three dimensions whether it is based on space-spectrum or space-time. The remote sensing data used in this way contains three dimensions, two of which are space and the other is time or spectrum, therefore the requirement can be met by using a storage method based on a three-dimensional storage structure. However, for the four-dimensional remote sensing data based on the time-space-spectral and processed by data processing, if still stored as the three-dimensional remote sensing data, it is necessary to become a three-dimensional cube set in one time sequence or one spectral sequence by reducing the dimension of the spectral dimension or time dimension. Such storage method does not have multidimensional analysis of time, space and spectrum, and is inconvenient to efficiently manage, organize, extract and operate the four-dimensional remote sensing data.